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17 pages, 2610 KiB  
Review
The Design and Analysis of the Fabrication of Micro- and Nanoscale Surface Structures and Their Performance Applications from a Bionic Perspective
by Haohua Zheng, Jiawei Liu and Yake Qiu
Materials 2024, 17(16), 4014; https://doi.org/10.3390/ma17164014 - 12 Aug 2024
Viewed by 331
Abstract
This paper comprehensively discusses the fabrication of bionic-based ultrafast laser micro–nano-multiscale surface structures and their performance analysis. It explores the functionality of biological surface structures and the high adaptability achieved through optimized self-organized biomaterials with multilayered structures. This study details the applications of [...] Read more.
This paper comprehensively discusses the fabrication of bionic-based ultrafast laser micro–nano-multiscale surface structures and their performance analysis. It explores the functionality of biological surface structures and the high adaptability achieved through optimized self-organized biomaterials with multilayered structures. This study details the applications of ultrafast laser technology in biomimetic designs, particularly in preparing high-precision, wear-resistant, hydrophobic, and antireflective micro- and nanostructures on metal surfaces. Advances in the fabrications of laser surface structures are analyzed, comparing top-down and bottom-up processing methods and femtosecond laser direct writing. This research investigates selective absorption properties of surface structures at different scales for various light wavelengths, achieving coloring or stealth effects. Applications in dirt-resistant, self-cleaning, biomimetic optical, friction-resistant, and biocompatible surfaces are presented, demonstrating potential in biomedical care, water-vapor harvesting, and droplet manipulation. This paper concludes by highlighting research frontiers, theoretical and technological challenges, and the high-precision capabilities of femtosecond laser technology in related fields. Full article
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<p>Organisms with specialized functional surfaces and their microstructures [<a href="#B23-materials-17-04014" class="html-bibr">23</a>]. Reproduced with permission from Dong Wu. Bioinspired micro-/nanostructured surfaces prepared by femtosecond laser direct writing for multi-functional applications; published by IOP Publishing Ltd., 2020.</p>
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<p>SEM images of three LIPSS structures produced under different laser irradiation conditions, distinguished fundamentally by the laser fluence and pulse number Neff_2D (parameters given in the text). (<b>A</b>) ripples with a periodicity Λy = 850 nm, (<b>B</b>) grooves, Λy = 840 nm, Λx = 2.6 μm, and (<b>C</b>) spikes. (<b>D</b>) Highly irregular (“damaged”) morphology (optical micrograph) obtained at high fluence and Neff_2D values. (<b>E</b>) Schematic distribution of the different structures found with one single scan, depending on the laser fluence and the effective number of pulses in an area (Neff_2D). The positions on this plot for the structures shown in (<b>A</b>–<b>D</b>) are represented accordingly. The scanning direction and laser polarization for all the images shown are included in (<b>A</b>) [<a href="#B42-materials-17-04014" class="html-bibr">42</a>]. Reproduced with permission from Jan Siegel. Biomimetic surface structures in steel fabricated with femtosecond laser pulses: influence of laser rescanning on morphology and wettability; published by Beilstein-Institut, 2018.</p>
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<p>(<b>a</b>) Measured contact-angle values as a function of laser energy density. (<b>b</b>) Contact-angle values obtained from measurements of different surface roughness values at the same energy injection [<a href="#B55-materials-17-04014" class="html-bibr">55</a>].</p>
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<p>Characterization of VSMCs and HUVECs. (<b>a</b>) AFM image of the morphology of VSMCs (The dashed lines 1-1’ and 2-2’ serve as size reference scales.); (<b>b</b>) line contours of individual lines in (<b>a</b>); (<b>c</b>) diameters of the fiber structures (The blue dashed line indicates the peak diameters of the two fiber structures.); (<b>d</b>) height distribution of the cell surface; (<b>e</b>) AFM image of the morphology of HUVECs(The dashed line serves as a size reference scale.); (<b>f</b>) line contours of the white dashed lines in (<b>e</b>). [<a href="#B61-materials-17-04014" class="html-bibr">61</a>] Reproduced with permission from Chunyong Liang, Biomimetic cardiovascular stents for in vivo re-endothelialization; published by Elsevier Ltd., 2016.</p>
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<p>The application of the motion detection of TPSs for recognizing gestures and gaits [<a href="#B70-materials-17-04014" class="html-bibr">70</a>]. Reproduced with permission from Saihua Jiang. Facile monitoring of human motions on a fireground by using an MiEs-TENG sensor; published by Elsevier Ltd., 2021.</p>
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17 pages, 29624 KiB  
Article
Analysis of Chip Morphology in Heavy Milling of 508III Steel Considering Different Tool Wear Conditions
by Rui Guan, Yaonan Cheng, Jing Xue, Shilong Zhou, Xingwei Zhou and Wenjie Zhai
Materials 2024, 17(16), 3948; https://doi.org/10.3390/ma17163948 - 8 Aug 2024
Viewed by 251
Abstract
During the process of chip formation, the chip is subjected to extrusion pressure, friction, heat, and a strong chemical reaction. The chip’s macro and micro morphology, to a certain extent, reflect the condition of the tool during the cutting procedure. Therefore, researching the [...] Read more.
During the process of chip formation, the chip is subjected to extrusion pressure, friction, heat, and a strong chemical reaction. The chip’s macro and micro morphology, to a certain extent, reflect the condition of the tool during the cutting procedure. Therefore, researching the macroscopic and microscopic morphology of the chip’s surface in response to different tool wear conditions is of great significance to reproducing the cutting condition and analyzing the tool wear mechanism. This paper focuses on the chips formed by milling the difficult-to-machine material 508III high-strength steel. Firstly, the 508III steel milling experiment is carried out at the actual machining site to collect chip data under different tool wear conditions. Next, the free surface morphology of chips and the bottom surface morphology of chips are analyzed. Further, the chip edges are investigated, and their causes are analyzed. Finally, heavy milling 508III steel chip curl morphology analysis is performed. The research results play important roles in revealing the mechanism of tool wear and the relationship between chip morphology and tool wear. This information can be used to provide theoretical and technical support for monitoring the tool wear status based on chip morphology. Full article
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<p>Milling experiment site.</p>
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<p>Chip morphology and tool wear under different passes.</p>
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<p>Three-dimensional schematic diagram of chips formation.</p>
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<p>Free surface morphology of chips with different number of passes. (<b>a</b>) Free surface morphology of the chip at the second pass. (<b>b</b>) Free surface morphology of the chip at the 8th pass. (<b>c</b>) Free surface morphology of the chip at the 18th pass.</p>
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<p>SEM image of the free surface of the chip with different numbers of tool strokes. (<b>a</b>–<b>c</b>) Free surface of C2, C8 and C18 chips. (<b>a1</b>–<b>c1</b>) A larger view of upper free surface of C2, C8 and C18 chips. (<b>a2</b>–<b>c2</b>) A larger view of lower end of free surface of C2, C8 and C18 chips.</p>
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<p>Bottom surface morphology of chips under different numbers of passes. (<b>a</b>) Bottom surface of the chip under the second pass. (<b>b</b>) Bottom surface of the chip under the 8th pass. (<b>c</b>) Bottom surface of the chip under the 18th pass.</p>
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<p>Microscopic morphology of the bottom surface of the chip under the 6th pass.</p>
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<p>Chip morphology and energy spectrum analysis at the 16th pass.</p>
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<p>SEM images of chip bottom surface at different tool wear stages. (<b>a</b>–<b>c</b>) Bottom surface of C2, C8 and C18 chips. (<b>a1</b>–<b>c1</b>) A larger view of bottom surface of upper in C2, C8 and C18 chips. (<b>a2</b>–<b>c2</b>) A larger view of bottom surface of lower end in C2, C8 and C18 chips.</p>
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<p>Chip bottom surface morphology.</p>
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<p>Chip edge profile under different number of passes.</p>
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<p>C6 chip non-free end chip edges.</p>
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<p>Chip curl pattern with partial number of passes.</p>
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20 pages, 14233 KiB  
Article
Large-Deformation Modeling of Surface Instability and Ground Collapse during Tunnel Excavation by Material Point Method
by Haipeng Luo, Shimin Zhang, Miaomiao Sun, Shilin Gong and Chengbao Hu
Buildings 2024, 14(8), 2414; https://doi.org/10.3390/buildings14082414 - 5 Aug 2024
Viewed by 445
Abstract
Recent rapid urbanization has led to an increase in tunnel construction, escalating the prevalence of ground collapses. Ground collapses, characterized by large deformation and strain-softening, pose a significant challenge for classical numerical theories and simulation methods. Consequently, a numerical framework combining the material [...] Read more.
Recent rapid urbanization has led to an increase in tunnel construction, escalating the prevalence of ground collapses. Ground collapses, characterized by large deformation and strain-softening, pose a significant challenge for classical numerical theories and simulation methods. Consequently, a numerical framework combining the material point method (MPM) and strain-softening Drucker–Prager plasticity is introduced in this study to more accurately describe the evolution process and failure mechanism of the subgrade during tunnel excavation. The proposed numerical framework was validated against an analytic solution employing a typical ‘dry bottom’ dam model with solid non-linearity and large deformation; some of the results are also compared with those of the SPH method and centrifugal modeling tests to verify the validity of the MPM method in this paper. The validated model was used in this study to conduct a comprehensive analysis of surface instability and ground collapse under varying soil conditions. This included factors such as strata thickness, cohesion, internal friction angle, and a quantitative description of the development of longitudinal subsidence of the surface. The aim was to clarify deformation responses, failure patterns, and excavation mechanisms, providing insights for underground tunneling practices. Full article
(This article belongs to the Special Issue Numerical Modeling in Mechanical Behavior and Structural Analysis)
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<p>Representative incidents of tunnel-induced road collapse in (<b>a</b>) Foshan, (<b>b</b>) Hangzhou, (<b>c</b>) Xiamen, and (<b>d</b>) Xi’an [<a href="#B5-buildings-14-02414" class="html-bibr">5</a>,<a href="#B6-buildings-14-02414" class="html-bibr">6</a>,<a href="#B7-buildings-14-02414" class="html-bibr">7</a>,<a href="#B8-buildings-14-02414" class="html-bibr">8</a>].</p>
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<p>Schematic illustrating material behavior description based on MPM.</p>
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<p>The diagram illustrating MPM computation.</p>
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<p>The initial geometric configuration of a “dry bottom” dam.</p>
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<p>Comparisons of MPM and analytical solutions for the dam collapse problem.</p>
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<p>Initial configuration of the tunnel model.</p>
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<p>The spatial distribution of vertical stresses at a tunnel construction site under different overburden heights following the application of self-weight stress loads (δ<span class="html-italic">h</span> = 0 m): (<b>a</b>) <span class="html-italic">t</span> = 5 m, <span class="html-italic">t/l</span> = 0.5, (<b>b</b>) <span class="html-italic">t</span> = 10 m, <span class="html-italic">t/l</span> = 1.0, and (<b>c</b>) <span class="html-italic">t</span> = 20 m, <span class="html-italic">t/l</span> = 2.0.</p>
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<p>The spatial distribution of deviatoric strain at a tunnel construction site under various overburden heights after the application of self-weight stress loads (δ<span class="html-italic">h</span> = 0 m): (<b>a</b>) <span class="html-italic">t</span> = 5 m, <span class="html-italic">t/l</span> = 0.5, (<b>b</b>) <span class="html-italic">t</span> = 10 m, <span class="html-italic">t/l</span> = 1.0, and (<b>c</b>) <span class="html-italic">t</span> = 20 m, <span class="html-italic">t/l</span> = 2.0.</p>
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<p>Comparison between the final deformation patterns from (<b>a</b>) present MPM, (<b>b</b>) Zhang et al., SPH (2019) [<a href="#B47-buildings-14-02414" class="html-bibr">47</a>], and (<b>c</b>) Schofield, centrifuge test (1980) [<a href="#B52-buildings-14-02414" class="html-bibr">52</a>].</p>
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<p>Comparison between the shear bands from (<b>a</b>) present MPM, (<b>b</b>) Zhang et al., SPH (2019) [<a href="#B47-buildings-14-02414" class="html-bibr">47</a>], and (<b>c</b>) Idinger et al., centrifuge test (2011) [<a href="#B44-buildings-14-02414" class="html-bibr">44</a>].</p>
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<p>Final surface settlement under the conditions of (<b>a</b>) C/D = 0.5; (<b>b</b>) C/D = 1.0; (<b>c</b>) C/D = 2.0. (The mentioned references are Zhang et al. (2019) [<a href="#B47-buildings-14-02414" class="html-bibr">47</a>], Idinger et al. (2011) [<a href="#B44-buildings-14-02414" class="html-bibr">44</a>]).</p>
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<p>Final surface settlement under the conditions of (<b>a</b>) C/D = 0.5; (<b>b</b>) C/D = 1.0; (<b>c</b>) C/D = 2.0. (The mentioned references are Zhang et al. (2019) [<a href="#B47-buildings-14-02414" class="html-bibr">47</a>], Idinger et al. (2011) [<a href="#B44-buildings-14-02414" class="html-bibr">44</a>]).</p>
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<p>Comparison of (<b>a</b>) normalized deepest position; (<b>b</b>) normalized crater depth; (<b>c</b>) normalized crater width. (The mentioned references are Zhang et al. (2019) [<a href="#B47-buildings-14-02414" class="html-bibr">47</a>], Idinger et al. (2011) [<a href="#B44-buildings-14-02414" class="html-bibr">44</a>]).</p>
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<p>The process of ground collapse induced by the displacement disturbance of the tunnel face during construction (<span class="html-italic">t</span> = 5 m, <span class="html-italic">t/l</span> = 0.5): δ<span class="html-italic">h</span> = (<b>a</b>) 1.25, (<b>b</b>) 2.50, (<b>c</b>) 5.00, and (<b>d</b>) 10.00 m.</p>
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<p>The process of ground collapse induced by the displacement disturbance of the tunnel face during construction (<span class="html-italic">t</span> = 10 m, <span class="html-italic">t/l</span> = 1.0): δ<span class="html-italic">h</span> = (<b>a</b>) 1.25, (<b>b</b>) 2.50, (<b>c</b>) 5.00, and (<b>d</b>) 10.00 m.</p>
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<p>The process of ground collapse induced by the displacement disturbance of the tunnel face during construction (<span class="html-italic">t</span> = 20 m, <span class="html-italic">t/l</span> = 2.0): δ<span class="html-italic">h</span> = (<b>a</b>) 1.25, (<b>b</b>) 2.50, (<b>c</b>) 5.00, and (<b>d</b>) 10.00 m.</p>
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<p>The surface subsidence during tunnel construction varies with overburden heights: δ<span class="html-italic">h</span> = (<b>a</b>) 1.25, (<b>b</b>) 2.50, (<b>c</b>) 5.00, and (<b>d</b>) 10.00 m.</p>
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<p>Parameter variations at different deformation stages (<span class="html-italic">δ</span><sub>h</sub>/l) under different cover-to-diameter ratios (t/l). (<b>a</b>) Normalized crater depth (<span class="html-italic">δ</span><sub>d</sub>/l); (<b>b</b>) Normalized crater width (<span class="html-italic">δ</span><sub>w</sub>/l); (<b>c</b>) Normalized deepest position (<span class="html-italic">δ</span><sub>dis</sub>/l).</p>
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<p>The process of ground collapse induced by the displacement disturbance of the tunnel face during construction (<span class="html-italic">φ</span> = 10°): δ<span class="html-italic">h</span> = (<b>a</b>) 1.25, (<b>b</b>) 2.50, (<b>c</b>) 5.00, and (<b>d</b>) 10.00 m.</p>
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<p>The process of ground collapse induced by the displacement disturbance of the tunnel face during construction (<span class="html-italic">φ</span> = 40°): δ<span class="html-italic">h</span> = (<b>a</b>) 1.25, (<b>b</b>) 2.50, (<b>c</b>) 5.00, and (<b>d</b>) 10.00 m.</p>
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<p>Parameter variations at different deformation stages (<span class="html-italic">δ</span><sub>h</sub>/l) under different friction angles (<span class="html-italic">φ</span>). (<b>a</b>) Normalized crater depth(<span class="html-italic">δ</span><sub>d</sub>/l); (<b>b</b>) Normalized crater width (<span class="html-italic">δ</span><sub>w</sub>/l); (<b>c</b>) Normalized deepest position (<span class="html-italic">δ</span><sub>dis</sub>/l).</p>
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<p>The process of ground collapse induced by the displacement disturbance of the tunnel face during construction (<span class="html-italic">c</span> = 2 kPa): δ<span class="html-italic">h</span> = (<b>a</b>) 1.25, (<b>b</b>) 2.50, (<b>c</b>) 5.00, and (<b>d</b>) 10.00 m.</p>
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<p>The process of ground collapse induced by the displacement disturbance of the tunnel face during construction (<span class="html-italic">c</span> = 20 kPa): δ<span class="html-italic">h</span> = (<b>a</b>) 1.25, (<b>b</b>) 2.50, (<b>c</b>) 5.00, and (<b>d</b>) 10.00 m.</p>
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<p>Parameter variations at different deformation stages (<span class="html-italic">δ</span><sub>h</sub>/l) under different cohesion (<span class="html-italic">c</span>). (<b>a</b>) Normalized crater depth (<span class="html-italic">δ</span><sub>d</sub>/l); (<b>b</b>) Normalized crater width (<span class="html-italic">δ</span><sub>w</sub>/l); (<b>c</b>) Normalized deepest position (<span class="html-italic">δ</span><sub>dis</sub>/l)</p>
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12 pages, 8972 KiB  
Article
Grain Structure and Texture Evolution in the Bottom Zone of Dissimilar Friction-Stir-Welded AA2024-T351 and AA7075-T651 Joints
by Haoge Shou, Yaoyao Song, Chenghang Zhang, Pengfei Zhang, Wei Zhao, Xixia Zhu, Peng Shi and Shule Xing
Materials 2024, 17(15), 3750; https://doi.org/10.3390/ma17153750 - 29 Jul 2024
Viewed by 365
Abstract
High-strength dissimilar aluminum alloys are difficult to connect by fusion welding, while they can be successfully joined by friction stir welding (FSW). However, the asymmetrical deformation and heat input that occur during FSW result in the formation of a heterogeneous microstructure in their [...] Read more.
High-strength dissimilar aluminum alloys are difficult to connect by fusion welding, while they can be successfully joined by friction stir welding (FSW). However, the asymmetrical deformation and heat input that occur during FSW result in the formation of a heterogeneous microstructure in their welded zone. In this work, the grain structure and texture evolution in the bottom zones of dissimilar FSW AA2024-T351 and AA7075-T651 joints at different welding speeds (feeding speeds) were quantitatively investigated. The results indicated that dynamic recrystallization occurs in the bottom zones of dissimilar FSW joints, and equiaxed grains with low grain sizes are formed at the welding speed of 60–240 mm/min. A high fraction of the recrystallized grains were generated in the bottom zones of the joints at a low welding speed, while a high fraction of the substructured grains are produced at a high welding speed. Different types of shear textures are produced in the bottom zones of the joints; the number fraction of shear texture types depends on different welding speeds. This study helps to understand the mechanism of microstructure homogenization in dissimilar FSW joints and provides a basis for further improving the microstructure of the welded zone for engineering applications. Full article
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<p>Profiles of cross sections of the three joints: (<b>a</b>) 60 mm/min, (<b>b</b>) 100 mm/min and (<b>c</b>) 240 mm/min [<a href="#B25-materials-17-03750" class="html-bibr">25</a>] (Green square represents the EBSD analysis area).</p>
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<p>Band contrast and Euler angle (Euler angles 1, 2 and 3 represent the angle/° of rotation around ND, WD and TD, respectively) images of grain structure in the bottom zones of the three joints: (<b>a</b>,<b>b</b>) 60 mm/min, (<b>c</b>,<b>d</b>) 100 mm/min and (<b>e</b>,<b>f</b>) 240 mm/min [<a href="#B25-materials-17-03750" class="html-bibr">25</a>].</p>
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<p>Distribution of grain size in the bottoms of the three joints [<a href="#B25-materials-17-03750" class="html-bibr">25</a>].</p>
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<p>Misorientation angle in the bottoms of the three joints [<a href="#B25-materials-17-03750" class="html-bibr">25</a>].</p>
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<p>Distribution features of the grains in the bottom zones of the three joints: (<b>a</b>) 60 mm/min, (<b>b</b>) 100 mm/min and (<b>c</b>) 240 mm/min.</p>
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<p>Calculation results of different grain types shown in <a href="#materials-17-03750-f005" class="html-fig">Figure 5</a>.</p>
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<p>Distribution maps of local misorientation in the bottom zones of the three joints: (<b>a</b>) 60 mm/min, (<b>b</b>) 100 mm/min and (<b>c</b>) 240 mm/min.</p>
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<p>Calculation results of local misorientation shown in <a href="#materials-17-03750-f007" class="html-fig">Figure 7</a>.</p>
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<p>Distribution of shear texture components in the bottom zone of the three joints: (<b>a</b>) 60 mm/min, (<b>b</b>) 100 mm/min and (<b>c</b>) 240 mm/min.</p>
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<p>Calculation results of different shear texture components in <a href="#materials-17-03750-f009" class="html-fig">Figure 9</a>.</p>
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<p>Microhardness distribution of the cross sections of the joints [<a href="#B25-materials-17-03750" class="html-bibr">25</a>].</p>
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18 pages, 3312 KiB  
Article
A Novel Geothermal Wellbore Model Based on the Drift-Flux Approach
by Yin Yuan, Weiqing Li, Jiawen Zhang, Junkai Lei, Xianghong Xu and Lihan Bian
Energies 2024, 17(14), 3569; https://doi.org/10.3390/en17143569 - 20 Jul 2024
Viewed by 333
Abstract
Geothermal energy, being a clean energy source, has immense potential, and accurate wellbore modeling is crucial for optimizing the drilling process and ensuring safety. This paper presents a novel geothermal wellbore model based on the drift-flux approach, tested under three different temperature and [...] Read more.
Geothermal energy, being a clean energy source, has immense potential, and accurate wellbore modeling is crucial for optimizing the drilling process and ensuring safety. This paper presents a novel geothermal wellbore model based on the drift-flux approach, tested under three different temperature and pressure well conditions. The proposed model integrates the conservation equations of mass, momentum, and energy, incorporating the gas–liquid two-phase flow drift-flux model and heat transfer model. The key features include handling the heat transfer between the formation and the wellbore, addressing the slip relationship between the gas and liquid phases, and accounting for wellbore friction. The nonlinear equations are discretized using the finite difference method, and the highly nonlinear system is solved using the Newton–Raphson method. The numerical simulation, validation, and comparison with existing models demonstrate the enhanced accuracy of this model. In our tests, the model achieved a high accuracy in calculating the bottom-hole pressure and temperature, with mean relative errors (MREs) significantly lower than those of other models. For example, the MREs for the bottom-hole pressure and temperature of the Rongxi area well in Xiongan, calculated by this model, are 1.491% and 1.323%, respectively. These results offer valuable insights for optimizing drilling parameters and ensuring drilling safety. Comparisons indicate that this approach significantly outperforms others in capturing the complex dynamics of geothermal wellbores, making it a superior tool for geothermal energy development. Full article
(This article belongs to the Section H2: Geothermal)
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<p>Schematic of drilling circulation.</p>
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<p>Wellbore grid division.</p>
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<p>Xiongan Rongxi area well: (<b>a</b>) temperature results performance of different models; and (<b>b</b>) pressure results over time for different models.</p>
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<p>Comparison of the pressure–depth curves calculated by this model with the measured results.</p>
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<p>The pressure results calculated at different time points as a function of depth: (<b>a</b>) Akbar’s model; and (<b>b</b>) Tonkin’s model.</p>
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<p>SNLG87-29 well: (<b>a</b>) pressure vs. depth curve; and (<b>b</b>) temperature vs. depth curve.</p>
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<p>Well No. 6: (<b>a</b>) pressure vs. depth curve; and (<b>b</b>) temperature vs. depth curve.</p>
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24 pages, 959 KiB  
Article
Improving Solid-Phase Fluidization Prediction in Circulating Fluidized Bed Risers: Drag Model Sensitivity and Turbulence Modeling
by Aldo Germán Benavides-Morán and Santiago Lain
Mathematics 2024, 12(12), 1852; https://doi.org/10.3390/math12121852 - 14 Jun 2024
Viewed by 454
Abstract
This contribution underscores the importance of selecting an appropriate interphase momentum transfer model for accurately predicting the distribution of the solid phase in a full-scale circulating fluidized bed (CFB) riser equipped with a smooth C-type exit. It also explores other critical factors such [...] Read more.
This contribution underscores the importance of selecting an appropriate interphase momentum transfer model for accurately predicting the distribution of the solid phase in a full-scale circulating fluidized bed (CFB) riser equipped with a smooth C-type exit. It also explores other critical factors such as domain configuration, grid size, the scope of time averaging, and turbulence modulation. The flow in a cold-CFB riser is simulated using the Eulerian–Eulerian two-fluid model within a commercial CFD package. Particle interactions in the rapid-flow regime are determined utilizing the kinetic theory of granular flow while enduring particle contacts are accounted for by incorporating frictional stresses. The turbulent dynamics of the continuous phase are described using two-equation turbulence models with additional modulation terms. The three-dimensional computational domain replicates an actual CFB riser geometry where experimental measurements are available for particulate phase axial and radial solid concentration. The simulation results reveal that the choice of drag model correlation significantly impacts both axial and radial solid distribution. Notably, the energy-minimization multi-scale drag model accurately depicts the dense solid region at the bottom and core–annular flow structure in the upper part. The solid-phase fluidization is overestimated in the lower riser section when a 2D domain is utilized. Neglecting turbulence modulation terms in the k-ω SST model results in nearly flat solid volume fraction radial profiles in the analyzed upper sections of the riser, resembling those obtained with the k-ϵ model. Full article
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<p>Schematic of the CFB apparatus at TU Delft [<a href="#B5-mathematics-12-01852" class="html-bibr">5</a>].</p>
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<p>(<b>a</b>) Simplified computational domain of the CFB riser with relevant dimensions. (<b>b</b>) Three-dimensional geometry of the riser.</p>
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<p>(<b>a</b>) Axial profiles of solid-phase volume fraction computed with the EMMS drag model and different grid resolutions (Cases 2, 3, 5, and 8). (<b>b</b>) Root mean square error (RMSE) calculated along the riser height.</p>
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<p>Effect of time-averaging on the results of solid holdup. Simulation results from Case 5.</p>
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<p>Snapshots of solid-phase volume fraction along the vertical center-plane of the riser, extracted from Case 8.</p>
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<p>Snapshots of solid-phase axial velocity along the vertical center-plane of the riser, extracted from Case 8.</p>
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<p>Snapshots of gas axial velocity along the vertical center-plane of the riser, extracted from Case 8.</p>
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<p>(<b>a</b>) Radial profiles of solid volume fraction at five different elevations. Simulation results taken from Case 8. (<b>b</b>) Root mean square error (RMSE) for the radial profiles computed from Case 8.</p>
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<p>Solid-phase volume fraction along the riser (<b>a</b>), and radial profiles at three different elevations, (<b>b</b>–<b>d</b>). Open symbols represent experimental data [<a href="#B5-mathematics-12-01852" class="html-bibr">5</a>]. Numerical results obtained from cases in <a href="#mathematics-12-01852-t004" class="html-table">Table 4</a>. (<b>a</b>) solid volume fraction along the riser height (<span class="html-italic">H</span> = 4.1 m). (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.048</mn> </mrow> </semantics></math>. (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.40</mn> </mrow> </semantics></math>. (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <mi>H</mi> <mo>=</mo> <mn>0.64</mn> </mrow> </semantics></math>.</p>
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<p>RMSE computed for the radial profiles at the elevations shown in <a href="#mathematics-12-01852-f009" class="html-fig">Figure 9</a>b–d.</p>
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22 pages, 26927 KiB  
Article
Experimental Study on the Process of Submerged Arc Welding for Nickel-Based WC Flux-Cored Wire on Descaling Roll
by Chang Li, Lei Feng, Xing Han, Fenghua Luo and Han Sun
Coatings 2024, 14(6), 734; https://doi.org/10.3390/coatings14060734 - 8 Jun 2024
Viewed by 842
Abstract
Descaling roll is a key component used to remove iron oxide on billet surface in hot rolling production lines, and its surface properties have a significant effect on the quality of hot rolling products. The descaling roll is in bad service condition and [...] Read more.
Descaling roll is a key component used to remove iron oxide on billet surface in hot rolling production lines, and its surface properties have a significant effect on the quality of hot rolling products. The descaling roll is in bad service condition and subjected to the dynamic impact caused by high-pressure water erosion and high temperature billet descaling process for a long time. Under the action of high temperature, strong wear, multi-cycle heat, force, flow and multi-field strong coupling, the surface is prone to wear and corrosion failure, which affects the continuous rolling production. Submerged arc welding provides an effective way to repair and strengthen the descaling roll surface. The content of WC hard phase has a significant effect on welding quality. At the same time, direct submerged arc welding of Ni based WC wire on the descaling roll surface is easy to cause cracks, and a gradient synergistic strengthening effect can be formed by setting the transition bottom layer in welding. At present, there is a lack of experiments related to the preparation of flux-cored wire with different contents and the overlaying for the bottom submerged arc welding. Relevant studies are urgently needed to further reveal the welding process mechanism to provide significant theoretical support for the preparation of wire materials and the improvement of welding quality. In this paper, 30% and 60% WC flux-cored wires were prepared by employing Ni-Cr-B-Si alloy powder as the base powder, and submerged arc welding tests were conducted on the descaling roll, preparing three welding layers, namely 70% NiCrBSi + 30% WC without the bottom layer, 70% NiCrBSi + 30% WC with the bottom layer, and 40% NiCrBSi + 60% WC with the bottom layer. The properties of the welding layer were evaluated by SEM, XRD, EDS, hardness, friction and wear, corrosion and impact experiments. The results show that the WC hard phase added in the filler metal has dissolved and formed a new phase with other elements in the melting pool. The surfacing layer mainly contains Fe-Ni, Cr-C, Fe3Si, Ni3C and other phases. The surfacing layer prepared by a different amount of WC flux-cored wire and the surfacing layer with or without the bottom layer have great differences in microstructure and properties. This study lays a significant theoretical foundation for optimizing the submerged arc welding process and preparing welding materials for the descaling roll and has significant practical significance and application value. Full article
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<p>Schematic of submerged arc surfacing process.</p>
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<p>SEM and EDS results of 70% NiCrBSi + 30% WC powder: (<b>a</b>) 100× SEM results; (<b>b</b>) the EDS element surface scan result of local, corresponding to view (<b>a</b>).</p>
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<p>40% NiCrBSi + 60% WC powder SEM and EDS. (<b>a</b>) 200× SEM results; (<b>b</b>) the EDS element surface scan result of local, corresponding to view (<b>a</b>).</p>
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<p>XRD results of 70% NiCrBSi + 30% WC and 40% NiCrBSi + 60% WC powder.</p>
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<p>Flux-cored wire powder and wire preparation.</p>
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<p>Descaling roll submerged arc surfacing layer: (<b>a</b>) morphology of surfacing layer under different processes; (<b>b</b>) schematic of 70% NiCrBSi + 30% WC surfacing without the bottom layer; (<b>c</b>) schematic of 70% NiCrBSi + 30% WC with the bottom layer; (<b>d</b>) schematic of 40% NiCrBSi + 60% WC with the bottom layer.</p>
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<p>SEM of 70% NiCrBSi + 30% WC experimental block without the bottom layer: (<b>a</b>–<b>c</b>) the top microstructure of the surfacing layer; (<b>d</b>–<b>f</b>) the microstructure of the middle part surfacing layer; (<b>g</b>) the microstructure of the fusion zone surfacing layer and substrate.</p>
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<p>SEM and EDS of 70% NiCrBSi + 30% WC without the bottom layer. (<b>a</b>); 3000× SEM results; (<b>b</b>) the EDS element surface scan result, corresponding to view (<b>a</b>).</p>
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<p>SEM of 70% NiCrBSi + 30% WC with the bottom layer: (<b>a</b>–<b>c</b>) the microstructure of the surfacing layer middle part; (<b>d</b>–<b>f</b>) the microstructure of the fusion zone surfacing layer and substrate.</p>
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<p>SEM and EDS of 70% NiCrBSi + 30% WC with the bottom layer: (<b>a</b>) 3000× SEM results; (<b>b</b>) the EDS element surface scan result, corresponding to view (<b>a</b>).</p>
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<p>SEM of 40% NiCrBSi + 60% WC filler metal with the bottom layer: (<b>a</b>–<b>c</b>) The microstructure of the middle surfacing layer; (<b>d</b>) the structure morphology at the fusion zone of the surfacing layer and gradient lap; (<b>e</b>) the microstructure of the fusion zone between the gradient lap layer and the substrate; (<b>f</b>) the enlarged region, corresponding to view (<b>e</b>).</p>
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<p>SEM and EDS 40% NiCrBSi + 60% WC filler metal with the bottom layer: (<b>a</b>) 10,000× SEM results; (<b>b</b>) the EDS element surface scan result, corresponding to view (<b>a</b>).</p>
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<p>XRD of experimental block of submerged arc welding of descaling rollers.</p>
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<p>Hardness points of 70% NiCrBSi + 30% WC filler metal without the bottom layer.</p>
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<p>Hardness points of 70% NiCrBSi + 30% WC filler metal with the bottom layer.</p>
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<p>Hardness points of 40% NiCrBSi + 60% WC filler metal with the bottom layer.</p>
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<p>Magnified hardness indentation: (<b>a</b>) 70% NiCrBSi + 30% WC filler metal without the bottom layer; (<b>b</b>) 70% NiCrBSi + 30% WC filler metal with the bottom layer; (<b>c</b>) 40% NiCrBSi + 60% WC filler metal with the bottom layer.</p>
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<p>Hardness distribution curve.</p>
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<p>Variation curve of friction coefficient for welding layer.</p>
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<p>Abrasion morphology of welding layer with different WC contents: (<b>a</b>) the morphology of wear marks on 70% NiCrBSi + 30% WC filler metal without the bottom layer; (<b>b</b>) the morphology of wear marks on 70% NiCrBSi + 30% WC filler metal with the bottom layer; (<b>c</b>) the morphology of wear marks on 40% NiCrBSi + 60% WC filler metal with the bottom layer; (<b>d</b>) the 3D wear mark topography of 70% NiCrBSi + 30% WC filler metal without the bottom layer; (<b>e</b>) the 3D wear mark topography of 70% NiCrBSi + 30% WC filler metal with the bottom layer; (<b>f</b>) the 3D wear mark topography of 40% NiCrBSi + 60% WC filler metal with the bottom layer.</p>
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<p>Polarization curves and open circuit potential–time curve of welding layer with different WC contents. (<b>a</b>) Polarization curves; (<b>b</b>) open circuit potential-time curve.</p>
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<p>Experimental curves and experiment blocks of 70% NiCrBSi + 30% WC without the bottom layer.</p>
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<p>Fracture microstructure of 70% NiCrBSi + 30% WC filler metal without the bottom layer: (<b>a</b>–<b>f</b>) SEM results at different locations of the fracture.</p>
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<p>Hardness points of 70% NiCrBSi + 30% WC with the bottom layer.</p>
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<p>Fracture microstructure of 70% NiCrBSi + 30% WC filler metal with the bottom layer: (<b>a</b>–<b>f</b>) SEM results at different locations of the fracture.</p>
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13 pages, 4017 KiB  
Article
Effects of Oil Concentration in Flood Cooling on Cutting Force, Tool Wear and Surface Roughness in GTD-111 Nickel-Based Superalloy Slot Milling
by Gábor Kónya and Zsolt F. Kovács
J. Manuf. Mater. Process. 2024, 8(3), 119; https://doi.org/10.3390/jmmp8030119 - 7 Jun 2024
Viewed by 671
Abstract
Cooling–lubricating processes have a big impact on cutting force, tool wear, and the quality of the machined surface, especially for hard-to-machine superalloys, so the choice of the right cooling–lubricating method is of great importance. Nickel-based superalloys are among the most difficult materials to [...] Read more.
Cooling–lubricating processes have a big impact on cutting force, tool wear, and the quality of the machined surface, especially for hard-to-machine superalloys, so the choice of the right cooling–lubricating method is of great importance. Nickel-based superalloys are among the most difficult materials to machine due to their high hot strength, work hardening, and extremely low thermal conductivity. Previous research has shown that flood cooling results in the least tool wear and cutting force among different cooling–lubricating methods. Thus, the effects of the flood oil concentration (3%; 6%; 9%; 12%; and 15%) on the above-mentioned factors were investigated during the slot milling of the GTD-111 nickel-based superalloy. The cutting force was measured during machining with a Kistler three-component dynamometer, and then after cutting the tool wear and the surface roughness on the bottom surface of the milled slots were measured with a confocal microscope and tactile roughness tester. The results show that at a 12% oil concentration, the tool load and tool wear are the lowest; even at an oil concentration of 15%, a slight increase is observed in both factors. Essentially, a higher oil concentration reduces friction between the tool and the workpiece contact surface, resulting in reduced tool wear and cutting force. Furthermore, due to less friction, the heat generation in the cutting zone is also reduced, resulting in a lower heat load on the tool, which increases tool life. It is interesting to note that the 6% oil concentration had the highest cutting force and tool wear, and strong vibration was heard during machining, which is also reflected in the force signal. The change in oil concentration did not effect the surface roughness. Full article
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<p>Factors affecting machinability.</p>
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<p>Experimental procedure.</p>
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<p>(<b>a</b>) <span class="html-italic">F</span><sub>x</sub>; (<b>b</b>) <span class="html-italic">F</span><sub>y</sub>; (<b>c</b>) <span class="html-italic">F</span><sub>z</sub>; and (<b>d</b>) <span class="html-italic">F</span> resulting cutting force as a function of machining time for an axial depth of cut of 8 mm.</p>
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<p>Broken tool at 3% emulsion concentration.</p>
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<p>(<b>a</b>) <span class="html-italic">F</span><sub>x</sub>; (<b>b</b>) <span class="html-italic">F</span><sub>y</sub>; (<b>c</b>) <span class="html-italic">F</span><sub>z</sub>; and (<b>d</b>) resulting cutting force as a function machining time for an axial depth of cut of 4 mm.</p>
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<p>Main cutting force as a function of oil concentration.</p>
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<p>Microscopic images of the tools used at (<b>a</b>) 3%; (<b>b</b>) 6%; (<b>c</b>) 9%; (<b>d</b>) 12%; and (<b>e</b>) 15% oil concentrations.</p>
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<p>Tool wear as a function of oil concentration.</p>
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<p>Average surface roughness (<span class="html-italic">R</span><sub>a</sub>) and main roughness depth (<span class="html-italic">R</span><sub>z</sub>) as a function of the oil concentration.</p>
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22 pages, 17643 KiB  
Article
Response of Shallow-Water Temperature and Significant Wave Height to Sequential Tropical Cyclones in the Northeast Beibu Gulf
by Xiaotong Chen, Lingling Xie, Mingming Li, Ying Xu and Yulin Wang
J. Mar. Sci. Eng. 2024, 12(5), 790; https://doi.org/10.3390/jmse12050790 - 8 May 2024
Viewed by 746
Abstract
Using shallow-water buoy observations, reanalysis data, and numerical models, this study analyzes the variations in sea temperature and significant wave height (SWH) caused by two sequential tropical cyclones (TCs) ‘Lionrock’ and ‘Kompasu’ in October 2021 in the northeast Beibu Gulf, South China Sea. [...] Read more.
Using shallow-water buoy observations, reanalysis data, and numerical models, this study analyzes the variations in sea temperature and significant wave height (SWH) caused by two sequential tropical cyclones (TCs) ‘Lionrock’ and ‘Kompasu’ in October 2021 in the northeast Beibu Gulf, South China Sea. The results show that the sea surface temperature (SST) cooling of the nearshore waters was larger than the offshore water in the basin of the gulf, with the cooling amplitude and rate decreasing and the cooling time lagging behind wind increasing from coast to offshore. The near-surface temperature at the buoy station had a maximum decrease of 2.8 °C after ‘Lionrock’, and the decrease increased slightly to 3 °C after the stronger wind of ‘Kompasu’. The total decrease of 4.6 °C indicates that the sequential TCs had a superimposed effect on the cooling of the Beibu Gulf. The heat budget analysis revealed that the sea surface heat loss and the Ekman pumping rate in the nearshore waters during ‘Kompasu’ (−535 W/m2 and 5.8 × 10−4 m/s, respectively) were significantly higher than that (−418 W/m2 and 4 × 10−4 m/s) during ‘Lionrock’. On the other hand, the SST cooling (−1.2 °C) during the second TC is smaller than (−1.6 °C) the first weaker TC in the gulf basin, probably due to the deepening of the mixed layer. During the observation period, the waves in the Beibu Gulf were predominantly wind-driven. The maximum SWHs reached 1.58 m and 2.3 m at the bouy station near shore during the two TCs, and the SWH variation was highly correlated to the wind variation with a correlation of 0.95. The SWH increases from the nearshore to offshore waters during the TCs. The SAWN and ARCIRC coupled model results suggest that wave variations in the Beibu Gulf are primarily influenced by water depth, bottom friction, and whitecapping. Two days after the TCs, sea surface cooling and high waves appeared again due to a cold air event. Full article
(This article belongs to the Special Issue Ocean Observations)
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<p>Track and intensity changes of TCs, ‘Lionrock’ and ‘Kompasu’.</p>
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<p>(<b>a</b>–<b>h</b>) Daily SST and SST variations in the Beibu Gulf and its adjacent waters from 7 to 14 October 2021. The wind field is the momentary wind field at 24 h per day and the black arrow is the wind speed (m/s) and direction, and the white dotted dashed line is the 20 m water depth isobath. The curve represents the TC movement path, where the color represents the TC class (the color corresponds to the specific class; see the legend of <a href="#jmse-12-00790-f001" class="html-fig">Figure 1</a>). The red dot is the location of the buoy.</p>
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<p>(<b>a</b>) Variations of wind at the nearshore buoy observation station in the Beibu Gulf in October 2021. The gray shadow is the period when the buoy was affected by ‘Lionrock’ (the wind speed was greater than 9.12 m/s, the same as below), and the pink shadow is the period when the buoy was affected by ‘Kompasu’. The blue solid line is the reanalyzed ERA5 wind speed (m/s), and the red arrow is the wind direction; (<b>b</b>) Variations of temperature at the nearshore buoy observation station in the Beibu Gulf in October 2021. The red solid line is the buoy temperature (°C), and the blue solid line is the SST of the buoy station obtained via interpolation from CMEMS.</p>
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<p>(<b>a</b>–<b>h</b>) Changes in sea surface heat flux in Beibu Gulf from 7 to 14 October 2021. The color bar represents the heat flux value (W/m<sup>2</sup>), and the white dotted dashed line is the 20 m water depth isobath.</p>
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<p>Changes in each component of sea surface heat flux at the north buoy station in the Beibu Gulf in October 2021. The gray shadow is the period when the buoy was affected by ‘Lionrock’ (the wind speed was greater than 9.12 m/s), and the pink shadow is the period when the buoy was affected by ‘Kompasu’.</p>
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<p>Geopotential height and temperature distribution map of Beibu Gulf on 16 October (<b>a</b>) and 22 October (<b>b</b>), 2021. (Geopotential height unit: 10 m).</p>
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<p>(<b>a</b>–<b>h</b>) is the distribution of ERA5 wind field (vector arrows represent the wind field, and the position marked by a red pentagram in the figure is the center of the TC at that time) and Ekman pumping rate (the color bar represents the Ekman pumping rate) in the Beibu Gulf during ‘Lionrock’ and ‘Kompasu’ at 06:00 on 7–14 October 2021.</p>
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<p>(<b>a</b>–<b>f</b>) Distribution of SWH (m) and wind (black arrow size is wind speed (m/s) and direction) in Beibu Gulf and adjacent waters on 8–9 October 2021. The blue solid line is the ‘Lionrock’ track. The red dot is the location of the buoy.</p>
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<p>(<b>a</b>–<b>f</b>) Distribution of SWH (m) and wind (black arrow size is wind speed (m/s) and direction) in Beibu Gulf and adjacent waters on 12–13 October 2021. The blue solid line is the ‘Kompasu’ track. The red dot is the location of the buoy.</p>
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<p>(<b>a</b>)Variation of sea surface wind at Beibu Gulf Buoy Station in October 2021. Gray shading is the period when the buoy was affected by ‘Lionrock’, pink shading is the period when the buoy was affected by ‘Kompasu’, the blue solid line is the wind speed (m/s), and the red arrow is the wind direction; (<b>b</b>) Variation of SWH at Beibu Gulf Buoy Station in October 2021. The red solid line is the SWH observed by the buoy; the blue solid line is the SWH interpolated from CMEMS; the green solid line is the SWH computed by the SWAN model; and the black solid line is the SWH computed from the typhoon wave equation of Hao et al. [<a href="#B54-jmse-12-00790" class="html-bibr">54</a>].</p>
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<p>Wave energy input and dissipation at nearshore buoy station in the Beibu Gulf (<b>a</b>) and in offshore waters (<b>b</b>) on 6–14 October 2021. The gray shadow is the period when the buoy was affected by ‘Lionrock’ (the wind speed was greater than 9.12 m/s), and the pink shadow is the period when the buoy was affected by ‘Kompasu’.</p>
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19 pages, 8194 KiB  
Article
Numerical Study of Wheat Particle Flow Characteristics in a Horizontal Curved Pipe
by Dongming Xu, Yongxiang Li, Xuemeng Xu, Yongyu Zhang and Lei Yang
Processes 2024, 12(5), 900; https://doi.org/10.3390/pr12050900 - 28 Apr 2024
Cited by 1 | Viewed by 572
Abstract
Energy consumption is one of the important indicators of green development. The pressure drop and the particle kinetic energy loss in the pipe bend result in high energy consumption of wheat pneumatic conveying. In this paper, the CFD-DEM method is used to study [...] Read more.
Energy consumption is one of the important indicators of green development. The pressure drop and the particle kinetic energy loss in the pipe bend result in high energy consumption of wheat pneumatic conveying. In this paper, the CFD-DEM method is used to study the characteristics of flow field in horizontal pipe bend. The results show that the particles converge together under the force of the curved pipe wall to form a particle rope. With increasing pipe bend ratio R/D, the aggregation of particle bundles becomes stronger and the particle spiral phenomenon decreases. The particles impact the pipe wall at an angular position of θ = 30–60° around the bend, and their velocity decreases slowly under the friction resistance of the pipe wall. The velocity loss caused by particles impacting on the pipe wall increases with increasing initial velocity. When the particle mass flow rate is 1.26 kg/s and the gas velocity is 10 m/s, the pressure drop in the bend decreases and then increases with increasing R/D. The pressure drop of the bend is smallest for R/D = 2 and increases gradually with increasing gas-phase velocity. With increasing of R/D, the wall shear force between the particles and the bending pipe decreases and then increases, and the position of the maximum force moves towards the bottom of the bending pipe. The area over which the wall shear force acts continues to decrease because of the aggregation of particle bundles. The research results provide a theory for optimal design and application of pneumatic conveying equipment for wheat particles. Full article
(This article belongs to the Topic Multi-Phase Flow and Unconventional Oil/Gas Development)
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Graphical abstract

Graphical abstract
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<p>Experimental device used for pneumatic conveying: 1—compressor; 2—gas source; 3—Gas cooling dryer; 4—material silo; 5—experimental piping; 6—cyclone separator; 7—receiving bin.</p>
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<p>Photographs of equipment used in bending test: (<b>a</b>) high speed camera; (<b>b</b>) pressure sensor.</p>
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<p>(<b>a</b>) Wheat moisture measuring instrument and (<b>b</b>) physical size measurements (<b>c</b>) triaxial dimensions of wheat: Long, meddle and short are the dimensional values of the three directions in the three-dimensional space of the particle.</p>
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<p>Schematic of the contact between particles <span class="html-italic">p</span> and <span class="html-italic">q</span>.</p>
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<p>Collision process between a grain and the pipe wall.</p>
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<p>Geometrical model of pipe bend.</p>
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<p>Numerical grid of (<b>a</b>) longitudinal section and (<b>b</b>) transverse section.</p>
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<p>Wheat particles in EDEM.</p>
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<p>DEM particle calibration program.</p>
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<p>Comparison of particle velocities along curved pipes under different grid numbers.</p>
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<p>Computational procedure.</p>
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<p>(<b>a</b>) Simulation and (<b>b</b>) experimental data views of particle flow trajectory in bend.</p>
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<p>Comparison of simulated and experimental data.</p>
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<p>(<b>a</b>) Particle trajectory and (<b>b</b>) radial distribution of particles at different values of angle <span class="html-italic">θ</span> around pipe bend.</p>
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<p>Particle velocity at different values of angle <span class="html-italic">θ</span> around pipe bend.</p>
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<p>Variation of particle velocity in axial direction for <span class="html-italic">R</span>/<span class="html-italic">D</span> = 2.</p>
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<p>Flow-field pressure distribution and pressure drop around horizontal pipe bend for bend ratio <span class="html-italic">R</span>/<span class="html-italic">D</span> = (<b>a</b>) 1, (<b>b</b>) 2, (<b>c</b>) 3, (<b>d</b>) 4, (<b>e</b>) 5, (<b>f</b>) 6; (<b>g</b>) variation of pressure drop in pipe bend with <span class="html-italic">R</span>/<span class="html-italic">D</span>.</p>
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<p>Flow-field pressure distribution and pressure drop around horizontal pipe bend for bend ratio <span class="html-italic">R</span>/<span class="html-italic">D</span> = (<b>a</b>) 1, (<b>b</b>) 2, (<b>c</b>) 3, (<b>d</b>) 4, (<b>e</b>) 5, (<b>f</b>) 6; (<b>g</b>) variation of pressure drop in pipe bend with <span class="html-italic">R</span>/<span class="html-italic">D</span>.</p>
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<p>The pressure drop with the variation of gas-phase velocity.</p>
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<p>Variation of pressure drop in pipe bend with mass flow rates.</p>
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<p>Distribution of shear force between particles and pipe wall under different values of pipe bend ratio: bending diameter ratios: <span class="html-italic">R</span>/<span class="html-italic">D</span> = (<b>a</b>) 1, (<b>b</b>) 2, (<b>c</b>) 3, (<b>d</b>) 4, (<b>e</b>) 5, and (<b>f</b>) 6.</p>
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19 pages, 4449 KiB  
Article
The Application of Sand Transport with Cohesive Admixtures Model for Predicting Flushing Flows in Channels
by Leszek M. Kaczmarek, Jerzy Zawisza, Iwona Radosz, Magdalena Pietrzak and Jarosław Biegowski
Water 2024, 16(9), 1214; https://doi.org/10.3390/w16091214 - 24 Apr 2024
Cited by 1 | Viewed by 640
Abstract
The feature of self-cleansing in sewer pipes is a standard requirement in the design of drainage systems, as sediments deposited on the channel bottom cause changes in channel geometric properties and in hydrodynamic parameters, including the friction caused by the cohesive forces of [...] Read more.
The feature of self-cleansing in sewer pipes is a standard requirement in the design of drainage systems, as sediments deposited on the channel bottom cause changes in channel geometric properties and in hydrodynamic parameters, including the friction caused by the cohesive forces of sediment fractions. Here, it is shown that the content of cohesive fractions significantly inhibits the transport of non-cohesive sediments. This paper presents an advanced calculation procedure for estimating flushing flows in channels. This procedure is based on innovative predictive models developed for non-cohesive and granulometrically heterogeneous sediment transport with additional cohesive fraction content to estimate the magnitude of increased flow necessary to ensure self-cleansing of channels. The computations according to the proposed procedure were carried out for a wide range of hydrodynamic conditions, two grain diameters, six cohesive (clay) fraction additive contents and two critical stress values. The trend lines of calculations were composed with the results of experimental studies in hydraulic flumes. Full article
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<p>Vertical structure of sediment transport and the layer <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>z</mi> </mrow> </semantics></math> of erosion.</p>
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<p>Flow charts of numerical algorithms for calculations of sediment transport with/without cohesion by model [<a href="#B44-water-16-01214" class="html-bibr">44</a>] and [<a href="#B42-water-16-01214" class="html-bibr">42</a>,<a href="#B43-water-16-01214" class="html-bibr">43</a>], respectively; whereby: <math display="inline"><semantics> <mrow> <msubsup> <mi>τ</mi> <mo>∗</mo> <mo>′</mo> </msubsup> </mrow> </semantics></math>—the skin shear stress at the top of the contact layer; <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>i</mi> </msub> </mrow> </semantics></math>—content of grains with diameter <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>i</mi> </msub> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>r</mi> </msub> </mrow> </semantics></math>—representative diameter in grain collision sub-layer; <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <mn>0</mn> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math>—the shear stress at the top of grain collision sub-layer; <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>0</mn> </msub> </mrow> </semantics></math>—without cohesion; <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mrow> <mn>0</mn> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math>—with cohesion; <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>g</mi> </msub> </mrow> </semantics></math>—thickness of grain collision sub-layer; <math display="inline"><semantics> <mrow> <msubsup> <mi>u</mi> <mrow> <mi>f</mi> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math>—the shear velocity due to cohesion; <math display="inline"><semantics> <mi>N</mi> </semantics></math>—number of fraction; <math display="inline"><semantics> <mi>h</mi> </semantics></math>—water depth; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math>—critical Shields parameter; MPM formula—Meyer-Peter and Muller formula 1948 [<a href="#B14-water-16-01214" class="html-bibr">14</a>].</p>
Full article ">Figure 3
<p>Graphical representation of the assumption in the calculation of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>h</mi> </msub> <mo>=</mo> <mi>h</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>0</mn> </msub> <mo>=</mo> <mi>B</mi> </mrow> </semantics></math>.</p>
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<p>Flow charts of numerical algorithms for calculations of flushing flows <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mi>k</mi> </msub> </mrow> </semantics></math> where: <math display="inline"><semantics> <mi>n</mi> </semantics></math>—is Maning’s roughness <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mrow> <msup> <mi>m</mi> <mrow> <mfrac bevelled="true"> <mn>1</mn> <mn>3</mn> </mfrac> </mrow> </msup> <msup> <mi>s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </mfenced> </mrow> </semantics></math>, <math display="inline"><semantics> <mi>I</mi> </semantics></math>—slope of the bottom <math display="inline"><semantics> <mrow> <mfenced close="]" open="["> <mo>−</mo> </mfenced> </mrow> </semantics></math>; Kaczmarek et al. 2019 [<a href="#B41-water-16-01214" class="html-bibr">41</a>]; Zawisza et al. 2023a [<a href="#B42-water-16-01214" class="html-bibr">42</a>]; Zawisza et al. 2023b [<a href="#B43-water-16-01214" class="html-bibr">43</a>].</p>
Full article ">Figure 5
<p>Dependence <math display="inline"><semantics> <mrow> <mi>θ</mi> <msub> <mo>′</mo> <mo>∗</mo> </msub> <mi>c</mi> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <mi>η</mi> <mo> </mo> <mfenced close="]" open="["> <mo>%</mo> </mfenced> </mrow> </semantics></math> for the results of experiments Gdansk 2021 and Ghent 1998 adopted for calculations.</p>
Full article ">Figure 6
<p>Dependence <math display="inline"><semantics> <mrow> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>5</mn> <mo>%</mo> </mrow> </semantics></math>—Gdansk 2021 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> </mrow> </semantics></math>0.0025.</p>
Full article ">Figure 7
<p>(<b>a</b>) Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math>—Gdansk 2021 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> </mrow> </semantics></math> 0.0035. (<b>b</b>) Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math>—Ghent 1998 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.21</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mn>0.004</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 7 Cont.
<p>(<b>a</b>) Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math>—Gdansk 2021 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> </mrow> </semantics></math> 0.0035. (<b>b</b>) Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math>—Ghent 1998 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.21</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mn>0.004</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>15</mn> <mo>%</mo> </mrow> </semantics></math>—Gdansk 2021 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mn>0.0045</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>(<b>a</b>) Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math>—Gdansk 2021 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mn>0.006</mn> </mrow> </semantics></math>. (<b>b</b>) Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math>—Ghent 1998 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.21</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mn>0.009</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>30</mn> <mo>%</mo> </mrow> </semantics></math>—Ghent 1998 data; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.21</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mn>0.0125</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>30</mn> <mo>%</mo> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mn>0.0125</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>Dependence<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mo>Φ</mo> <mi>s</mi> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <mrow> <msub> <mrow> <msup> <mi>θ</mi> <mo>′</mo> </msup> </mrow> <mo>∗</mo> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>30</mn> <mo>%</mo> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <mn>0.21</mn> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mrow> <mo>∗</mo> <mi>c</mi> </mrow> <mo>′</mo> </msubsup> <mo>=</mo> <mn>0.0125</mn> </mrow> </semantics></math>.</p>
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21 pages, 8847 KiB  
Article
Analysis of Thermo-Hydrodynamic Lubrication of Three-Lobe Semi-Floating Ring Bearing Considering Temperature–Viscosity Effect and Static Pressure Flow
by Jiwei Dong, Huabing Wen, Junchao Zhu, Junhua Guo and Chen Zong
Lubricants 2024, 12(4), 140; https://doi.org/10.3390/lubricants12040140 - 18 Apr 2024
Viewed by 851
Abstract
High-power diesel engine turbochargers predominantly utilize floating ring bearings as their primary supporting components. To further enhance their load capacity, multi-lobe noncircular bearings have been progressively employed. This study focuses on the investigation of noncircular three-lobe SFRBs (semi-floating ring-bearing structures) in marine turbochargers. [...] Read more.
High-power diesel engine turbochargers predominantly utilize floating ring bearings as their primary supporting components. To further enhance their load capacity, multi-lobe noncircular bearings have been progressively employed. This study focuses on the investigation of noncircular three-lobe SFRBs (semi-floating ring-bearing structures) in marine turbochargers. Employing the half-step center Finite Difference Method (FDM) and the Newton–Raphson iterative procedure, the impact of operational parameters such as the journal speed, external load, oil supply pressure, and oil supply temperature on the static and dynamic characteristics of the inner oil film is analyzed. Subsequently, the accuracy of the theoretical model is validated through a comparative analysis of simulation results obtained from Dyrobes and Fluent. The findings indicate that as the oil supply pressure and temperature increase, the temperature rise and maximum oil film pressure of the three-lobe SFRBs gradually decrease, while the oil film thickness progressively increases, thereby significantly improving the lubrication state. The load capacity of the three-lobe SFRBs is primarily sustained by the bottom tile, where wall friction is most likely to occur. Additionally, within the actual speed range, the stiffness and damping of the three-lobe SFRBs exhibit noticeable nonlinear characteristics. Full article
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Figure 1

Figure 1
<p>Physical model of three-lobe SFRBs. (<b>a</b>) Actual photograph of three-lobe SFRBs; (<b>b</b>) schematic diagram of three-lobe SFRBs.</p>
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<p>Schematic diagram of oil film thickness and dynamic characteristics. (<b>a</b>) Angle calculation of inner film thickness; (<b>b</b>) coordinate system of dynamic coefficients calculation.</p>
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<p>Temperature–viscosity curve of CD40 oil.</p>
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<p>Flowchart of the calculation.</p>
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<p>Validation of pressure distribution. (<b>a</b>) Pressure distribution by program code; (<b>b</b>) pressure distribution by Fluent; (<b>c</b>) comparison of pressure distribution on the X−Y plane.</p>
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<p>Influence of four typical operating parameters on temperature rise. (<b>a</b>) Effect by Oil supply pressure; (<b>b</b>) effect by External load; (<b>c</b>) effect by Oil supply temperature; (<b>d</b>) effect by Journal speed.</p>
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<p>Influence of four typical operating parameters on power loss. (<b>a</b>) Effect by Oil supply pressure; (<b>b</b>) effect by External load; (<b>c</b>) effect by Oil supply temperature; (<b>d</b>) effect by Journal speed.</p>
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<p>Influence of four typical operating parameters on flow rate. (<b>a</b>) Effect by Journal speed; (<b>b</b>) effect by Oil supply pressure, blue and pink line are total and dynamic pressure flow rate respectively; (<b>c</b>) effect by External load; (<b>d</b>) effect by Oil supply temperature.</p>
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<p>Influence of four operating parameters on eccentricity ratio and attitude angle. (<b>a</b>) Effect by Oil supply pressure; (<b>b</b>) effect by Oil supply temperature; (<b>c</b>) effect by Journal speed; (<b>d</b>) effect by External load.</p>
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<p>Influence of four typical operating parameters on maximum pressure. (<b>a</b>) Effect by Oil supply pressure; (<b>b</b>) effect by External load; (<b>c</b>) effect by Oil supply temperature; (<b>d</b>) effect by Journal speed.</p>
Full article ">Figure 10 Cont.
<p>Influence of four typical operating parameters on maximum pressure. (<b>a</b>) Effect by Oil supply pressure; (<b>b</b>) effect by External load; (<b>c</b>) effect by Oil supply temperature; (<b>d</b>) effect by Journal speed.</p>
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<p>Influence of four typical operating parameters on minimum film thickness. (<b>a</b>) Effect by Oil supply pressure; (<b>b</b>) effect by External load; (<b>c</b>) effect by Oil supply temperature; (<b>d</b>) effect by Journal speed.</p>
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<p>Effect of oil supply temperature versus speed on the stiffness coefficients. (<b>a</b>) Direct stiffness coefficient <span class="html-italic">Kxx</span>; (<b>b</b>) direct stiffness coefficient <span class="html-italic">Kyy</span>; (<b>c</b>) cross stiffness coefficient <span class="html-italic">Kxy</span>; (<b>d</b>) cross stiffness coefficient <span class="html-italic">Kyx</span>.</p>
Full article ">Figure 13
<p>Effect of oil supply pressure versus speed on the stiffness coefficients. (<b>a</b>) Direct stiffness coefficient <span class="html-italic">Kxx</span>; (<b>b</b>) direct stiffness coefficient <span class="html-italic">Kyy</span>; (<b>c</b>) cross stiffness coefficient <span class="html-italic">Kxy</span>; (<b>d</b>) cross stiffness coefficient <span class="html-italic">Kyx</span>.</p>
Full article ">Figure 14
<p>Effect of oil supply temperature versus speed on the damping coefficients. (<b>a</b>) Direct damping coefficient <span class="html-italic">Cxx</span>; (<b>b</b>) direct damping coefficient <span class="html-italic">Cyy</span>; (<b>c</b>) cross damping coefficient <span class="html-italic">Cxy</span>; (<b>d</b>) cross damping coefficient <span class="html-italic">Cyx</span>.</p>
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<p>Effect of oil supply pressure versus speed on the damping coefficients. (<b>a</b>) Direct damping coefficient Cxx; (<b>b</b>) Direct damping coefficient Cyy; (<b>c</b>) Cross damping coefficient Cyx; (<b>d</b>) Cross damping coefficient Cxy.</p>
Full article ">Figure 15 Cont.
<p>Effect of oil supply pressure versus speed on the damping coefficients. (<b>a</b>) Direct damping coefficient Cxx; (<b>b</b>) Direct damping coefficient Cyy; (<b>c</b>) Cross damping coefficient Cyx; (<b>d</b>) Cross damping coefficient Cxy.</p>
Full article ">Figure A1
<p>Mesh of inner oil film.</p>
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25 pages, 10837 KiB  
Article
Integrated Modeling of Coastal Processes Driven by an Advanced Mild Slope Wave Model
by Michalis K. Chondros, Anastasios S. Metallinos and Andreas G. Papadimitriou
Modelling 2024, 5(2), 458-482; https://doi.org/10.3390/modelling5020025 - 11 Apr 2024
Viewed by 1213
Abstract
Numerical modeling of wave transformation, hydrodynamics, and morphodynamics in coastal regions holds paramount significance for combating coastal erosion by evaluating and optimizing various coastal protection structures. This study aims to present an integration of numerical models to accurately simulate the coastal processes with [...] Read more.
Numerical modeling of wave transformation, hydrodynamics, and morphodynamics in coastal regions holds paramount significance for combating coastal erosion by evaluating and optimizing various coastal protection structures. This study aims to present an integration of numerical models to accurately simulate the coastal processes with the presence of coastal and harbor structures. Specifically, integrated modeling employs an advanced mild slope model as the main driver, which is capable of describing all the wave transformation phenomena, including wave reflection. This model provides radiation stresses as inputs to a hydrodynamic model based on Reynolds-averaged Navier–Stokes equations to simulate nearshore currents. Ultimately, these models feed an additional model that can simulate longshore sediment transport and bed level changes. The models are validated against experimental measurements, including energy dissipation due to bottom friction and wave breaking; combined refraction, diffraction, and breaking over a submerged shoal; wave transformation and wave-generated currents over submerged breakwaters; and wave, currents, and sediment transport fields over a varying bathymetry. The models exhibit satisfactory performance in simulating all considered cases, establishing them as efficient and reliable integrated tools for engineering applications in real coastal areas. Moreover, leveraging the validated models, a numerical investigation is undertaken to assess the effects of wave reflection on a seawall on coastal processes for two ideal beach configurations—one with a steeper slope of 1:10 and another with a milder slope of 1:50. The numerical investigation reveals that the presence of reflected waves, particularly in milder bed slopes, significantly influences sediment transport, emphasizing the importance of employing a wave model that takes into account wave reflection as the primary driver for integrated modeling of coastal processes. Full article
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Figure 1

Figure 1
<p>Flowchart of the implementation sequence of numerical models.</p>
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<p>Simulated wave heights over a rippled bed, considering energy loss resulting from friction at the bottom (solid lines) and experimental measurements (points) of [<a href="#B66-modelling-05-00025" class="html-bibr">66</a>]. The dashed line denotes simulation results without considering the effect of bottom friction.</p>
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<p>Computed values (solid line) by HMS and experimental data (circular markers) by [<a href="#B67-modelling-05-00025" class="html-bibr">67</a>], for Case 1 (<b>top</b>) and Case 2 (<b>bottom</b>).</p>
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<p>Layout of the [<a href="#B68-modelling-05-00025" class="html-bibr">68</a>] experiments.</p>
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<p>Normalized wave heights as calculated by the HMS model (solid line) and as measured (points) for Test 5 of the [<a href="#B68-modelling-05-00025" class="html-bibr">68</a>] experiments, at the longitudinal transect A-A.</p>
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<p>Normalized wave heights as calculated by the HMS model (solid line) and as measured (points) for Test 5 of the [<a href="#B68-modelling-05-00025" class="html-bibr">68</a>] experiments, at six transverse transects.</p>
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<p>Normalized wave heights as calculated by the HMS model (solid line) and as measured (points) for Test 6 of the [<a href="#B68-modelling-05-00025" class="html-bibr">68</a>] experiments, at six transverse transects.</p>
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<p>Layout of the [<a href="#B72-modelling-05-00025" class="html-bibr">72</a>] experiment (<b>a</b>) with positions of measuring wave (#1-21) &amp; current speed (F, B and III) gauges and (<b>b</b>) bathymetry for the numerical implementation.</p>
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<p>Comparison of experimental measurements and numerical model results for (<b>a</b>) wave height and (<b>b</b>) current speed.</p>
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<p>Comparison of experimental measurements and numerical model results (Test 37) for (<b>a</b>) wave heights and (<b>b</b>) current speeds.</p>
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<p>Comparison of experimental measurements and numerical model results (Test 35) for (<b>a</b>) wave heights and (<b>b</b>) current speeds.</p>
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<p>Layout of the LSTF experiment of [<a href="#B74-modelling-05-00025" class="html-bibr">74</a>] (adapted from [<a href="#B75-modelling-05-00025" class="html-bibr">75</a>]).</p>
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<p>Comparison of experimental measurements and numerical model results for significant wave heights, longshore current speeds, and sediment transport rates of test BC1. The bathymetry of the coastal profile is given at the bottom of the Figure.</p>
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<p>Numerical layout of the T1C1 experiment of [<a href="#B74-modelling-05-00025" class="html-bibr">74</a>]. The orange dashed polygon denoted the area where the morphological bed evolution results will be evaluated.</p>
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<p>Comparison of measured (dashed lines) and simulated (solid lines) bed elevations at the end of the test T1C1 of the experiments conducted in [<a href="#B74-modelling-05-00025" class="html-bibr">74</a>].</p>
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<p>Cross-shore distribution of longshore current (<b>top</b>) and longshore sediment transport rate (<b>bottom</b>) for a 1:10 beach (Case 1a) without a seawall and various placements of a seawall along the surf zone.</p>
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<p>Cross-shore distribution of longshore current (<b>top</b>) and longshore sediment transport rate (<b>bottom</b>) for a 1:10 beach (Case 1b) without a seawall and various placements of a seawall along the surf zone.</p>
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<p>Cross-shore distribution of longshore current (<b>top</b>) and longshore sediment transport rate (<b>bottom</b>) for the case of a 1:50 (Case 2a) beach without a seawall and various placements of a seawall along the surf zone.</p>
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<p>Cross-shore distribution of longshore current (<b>top</b>) and longshore sediment transport rate (<b>bottom</b>) for the case of a 1:50 (Case 2b) beach without a seawall and various placements of a seawall along the surf zone.</p>
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18 pages, 8985 KiB  
Article
Sensitivity Analysis of the Square Cup Forming Process Using PAWN and Sobol Indices
by Tomás G. Parreira, Diogo C. Rodrigues, Marta C. Oliveira, Nataliya A. Sakharova, Pedro A. Prates and André F. G. Pereira
Metals 2024, 14(4), 432; https://doi.org/10.3390/met14040432 - 7 Apr 2024
Cited by 1 | Viewed by 704
Abstract
This study investigates the sensitivity of the square cup forming process. It analyses how the uncertainties in the material properties, friction and process conditions affect the results of the square cup, such as equivalent plastic strain, geometry change, thickness reduction, punch force and [...] Read more.
This study investigates the sensitivity of the square cup forming process. It analyses how the uncertainties in the material properties, friction and process conditions affect the results of the square cup, such as equivalent plastic strain, geometry change, thickness reduction, punch force and springback. The cup flange and the die curvature region are identified as highly affected areas, while the cup bottom is least affected by the uncertainties. Two sensitivity analysis techniques, PAWN and Sobol indices, are compared. In particular, the study shows that PAWN indices require a significantly smaller number of simulations than Sobol indices, making them a more efficient choice for sensitivity analysis. While both PAWN and Sobol indices generally give comparable results, discrepancies arise in the analysis of springback, where PAWN indices show superior accuracy, particularly when dealing with multimodal distributions. This observation highlights the importance of selecting the appropriate sensitivity analysis method based on the nature of the data being analysed. These results provide insights for optimizing stamping processes to reduce production time and costs. Full article
(This article belongs to the Section Metal Casting, Forming and Heat Treatment)
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Figure 1

Figure 1
<p>Square cup forming process: (<b>a</b>) dimensions of the tools in mm; (<b>b</b>) numerical model. adapted with permission from [<a href="#B18-metals-14-00432" class="html-bibr">18</a>]; (<b>c</b>) mesh sensitivity.</p>
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<p>Plots of mean and standard deviation for (<b>a</b>) EPS, (<b>b</b>) TR, (<b>c</b>) GC, (<b>d</b>) SB.</p>
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<p>Evolution of the mean (<b>a</b>) and standard deviation (<b>b</b>) of the force applied by the punch.</p>
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<p>Distribution of maximum model response for (<b>a</b>) PF; (<b>b</b>) EPS; (<b>c</b>) TR; (<b>d</b>) GC; and (<b>e</b>) SB.</p>
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<p>Stabilization analysis for (<b>a</b>) PF; (<b>b</b>) EPS; (<b>c</b>) TR; (<b>d</b>) GC; and (<b>e</b>) SB.</p>
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<p>Sobol and PAWN indices for (<b>a</b>) PF; (<b>b</b>) EPS; (<b>c</b>) TR; (<b>d</b>) GC; and (<b>e</b>) SB.</p>
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<p>Pareto analysis for PAWN indices for the responses (<b>a</b>) PF; (<b>b</b>) EPS; (<b>c</b>) TR; (<b>d</b>) GC; and (<b>e</b>) SB.</p>
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<p>Pareto analysis for Sobol indices for the responses (<b>a</b>) PF; (<b>b</b>) EPS; (<b>c</b>) TR; (<b>d</b>) GC; and (<b>e</b>) SB.</p>
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<p>Distribution of PAWN (<b>left</b>) and Sobol (<b>right</b>) indices for equivalent plastic strain: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>r</mi> </mrow> <mrow> <mn>90</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Distribution of PAWN (<b>left</b>) and Sobol (<b>right</b>) indices for thickness reduction: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>r</mi> </mrow> <mrow> <mn>90</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Distribution of PAWN (<b>left</b>) and Sobol (<b>right</b>) indices for the geometry changes: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>r</mi> </mrow> <mrow> <mn>90</mn> </mrow> </msub> </mrow> </semantics></math>; and (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>r</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Distribution of PAWN (<b>left</b>) and Sobol (<b>right</b>) indices for springback: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>H</mi> <mi>F</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>r</mi> </mrow> <mrow> <mn>90</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Parameters corresponding to the maximum PAWN index (<b>left</b>) and Sobol index (<b>right</b>) in each zone of for (<b>a</b>) EPS; (<b>b</b>) GC; (<b>c</b>) TR; and (<b>d</b>) SB.</p>
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<p>Indices of punch force as a function of displacement for (<b>a</b>) PAWN indices and (<b>b</b>) Sobol indices.</p>
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22 pages, 15149 KiB  
Article
Numerical Analysis of Ground Surcharge Effects on Deformation Characteristics in Shield Tunnel Linings
by Lixin Wei, Chunshan Yang, Weijie Chen, Liying Liu and Dong Su
Appl. Sci. 2024, 14(6), 2328; https://doi.org/10.3390/app14062328 - 10 Mar 2024
Viewed by 717
Abstract
To investigate the deformation characteristics of shield tunnel linings under ground surcharge, finite element software was employed to create a detailed three-dimensional model of the staggered assembly of the shield tunnel lining. This model includes components such as precast concrete segments, reinforcements, and [...] Read more.
To investigate the deformation characteristics of shield tunnel linings under ground surcharge, finite element software was employed to create a detailed three-dimensional model of the staggered assembly of the shield tunnel lining. This model includes components such as precast concrete segments, reinforcements, and joints (comprising bent bolts, washers, and bolt sleeves). Additionally, the model accounts for interface frictions between segments and the interactions between different rings. The reliability of the numerical model was verified based on the results of a full-scale model test. Additionally, the model accounts for interface frictions between segments and the interactions between different rings. Changes in tunnel convergence, joint tensioning, bolt stresses, reinforcement stresses, and concrete crack development were systematically analyzed. The results indicate the following: (1) the deformation mode of the lining structure under ground surcharge resembles a “transverse ellipse”. Joints located near the haunch opened along the outer arc, while those near the vault and bottom opened along the inner arc. The restraining effect of the bolts on joints opening in the inner arc was greater than that on the outer arc. Notably, when the opening of the inner arc reached 4.9 mm, the bolt stress escalated to the yield strength of 640 Mpa. (2) Under larger loads, the lining structure’s joints are susceptible to greater deformation, resulting in the tensile yielding of local reinforcement within these joints. (3) Cracks predominantly occur near the haunch, vault, and bottom of the lining structure, with the central angle of crack distribution ranging between 70° and 85°. Full article
(This article belongs to the Special Issue Advances in Tunnel and Underground Engineering)
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Figure 1

Figure 1
<p>Schematic diagram of lining blocking and joint construction. (<b>a</b>) Ring 1; (<b>b</b>) Ring 2; (<b>c</b>) Ring 3; (<b>d</b>) joint construction.</p>
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<p>Finite element model. (<b>a</b>) Full ring; (<b>b</b>) segment.</p>
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<p>Damage behavior of the concrete C50. (<b>a</b>) Compression damage; (<b>b</b>) tension damage.</p>
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<p>Schematic diagram of interactions.</p>
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<p>Schematic diagram of segment full-scale model’s test loading. (<b>a</b>) Segment full-scale model test; (<b>b</b>) distribution of loading beam; (<b>c</b>) distribution of test loading.</p>
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<p>Comparison of tunnel convergence from the numerical analyses and the full-scale test.</p>
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<p>The schematic diagram of the semi-infinite space subjected to a vertical concentrated force.</p>
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<p>Schematic diagram of the ground surcharge calculation model. (<b>a</b>) Cross section; (<b>b</b>) plan.</p>
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<p>Variation curves of additional pressures under ground surcharge. (<b>a</b>) Vertical additional pressure (<span class="html-italic">H</span> = 15 m); (<b>b</b>) horizontal additional pressure (<span class="html-italic">S</span> = 3 m).</p>
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<p>Schematic diagram of load distribution. (<b>a</b>) Normal condition; (<b>b</b>) additional pressure under ground surcharge.</p>
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<p>Distribution of lining structure displacement (deformation magnified by 10 times). (<b>a</b>) Case C1 (<span class="html-italic">P</span><sub>0</sub> = 120 kPa); (<b>b</b>) Case C4 (<span class="html-italic">P</span><sub>0</sub> = 480 kPa); (<b>c</b>) Case C7 (<span class="html-italic">P</span><sub>0</sub> = 840 kPa); (<b>d</b>) Case C10 (<span class="html-italic">P</span><sub>0</sub> = 120 kPa).</p>
Full article ">Figure 11 Cont.
<p>Distribution of lining structure displacement (deformation magnified by 10 times). (<b>a</b>) Case C1 (<span class="html-italic">P</span><sub>0</sub> = 120 kPa); (<b>b</b>) Case C4 (<span class="html-italic">P</span><sub>0</sub> = 480 kPa); (<b>c</b>) Case C7 (<span class="html-italic">P</span><sub>0</sub> = 840 kPa); (<b>d</b>) Case C10 (<span class="html-italic">P</span><sub>0</sub> = 120 kPa).</p>
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<p>Variation in horizontal convergence with <span class="html-italic">P</span><sub>0</sub>.</p>
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<p>Variation in joint opening with <span class="html-italic">P</span><sub>0</sub>.</p>
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<p>Variation in bolt stress with <span class="html-italic">P</span><sub>0</sub>. (<b>a</b>) Tensile stress; (<b>b</b>) Compressive stress.</p>
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<p>Distribution of bolt stress. (<b>a</b>) Pre-loading; (<b>b</b>) Case C2 (<span class="html-italic">P</span><sub>0</sub> = 240 kPa); (<b>c</b>) Case C4 (<span class="html-italic">P</span><sub>0</sub> = 480 kPa); (<b>d</b>) Case C6 (<span class="html-italic">P</span><sub>0</sub> = 720 kPa); (<b>e</b>) Case C8 (<span class="html-italic">P</span><sub>0</sub> = 960 kPa); (<b>f</b>) Case C10 (<span class="html-italic">P</span><sub>0</sub> = 1200 kPa).</p>
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<p>Variation in rebar stress with <span class="html-italic">P</span><sub>0</sub>. (<b>a</b>) Tensile stress; (<b>b</b>) Compressive stress.</p>
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<p>Distribution of rebar stress. (<b>a</b>) Pre-loading; (<b>b</b>) Case C2 (<span class="html-italic">P</span><sub>0</sub> = 240 kPa); (<b>c</b>) Case C4 (<span class="html-italic">P</span><sub>0</sub> = 480 kPa); (<b>d</b>) Case C6 (<span class="html-italic">P</span><sub>0</sub> = 720 kPa); (<b>e</b>) Case C8 (<span class="html-italic">P</span><sub>0</sub> = 960 kPa); (<b>f</b>) Case C10 (<span class="html-italic">P</span><sub>0</sub> = 1200 kPa).</p>
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<p>Development of concrete cracks (tension damage parameter <span class="html-italic">D</span><sub>t</sub>). (<b>a</b>) Case C1 (<span class="html-italic">P</span><sub>0</sub> = 140 kPa); (<b>b</b>) Case C2 (<span class="html-italic">P</span><sub>0</sub> = 240 kPa); (<b>c</b>) Case C3 (<span class="html-italic">P</span><sub>0</sub> = 360 kPa); (<b>d</b>) Case C4 (<span class="html-italic">P</span><sub>0</sub> = 480 kPa); (<b>e</b>) Case C5 (<span class="html-italic">P</span><sub>0</sub> = 600 kPa); (<b>f</b>) Case C6 (<span class="html-italic">P</span><sub>0</sub> = 720 kPa); (<b>g</b>) Case C7 (<span class="html-italic">P</span><sub>0</sub> = 840 kPa); (<b>h</b>) Case C8 (<span class="html-italic">P</span><sub>0</sub> = 960 kPa); (<b>i</b>) Case C9 (<span class="html-italic">P</span><sub>0</sub> = 1080 kPa); (<b>j</b>) Case C10 (<span class="html-italic">P</span><sub>0</sub> = 1200 kPa).</p>
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